SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 481–490 of 1718 papers

TitleStatusHype
Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach—0
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified